from datetime import datetime
from IPython.display import display
from IPython.display import Markdown as MD
current_date = datetime.now().strftime('%Y-%m-%d')
version = datetime.now().strftime('%Y%m%d.%H%M')
display(MD(f"**Date:** {current_date}"))
display(MD(f"**version:** {version}"))

# this code to will import all the things i need for this notebook

import os
import re
import math

import numpy as np
import pandas as pd

import random
from collections import Counter


# for the notebook rendering 
from IPython.display import display, HTML, FileLink, Markdown
from IPython.display import FileLink as FL
from IPython.display import Markdown as MD


# Graphs and Charts
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
import seaborn as sns
import plotly.express as px
# use to export plotly graphs
import plotly.io as pio 

#misc
from scipy.stats import spearmanr, kendalltau
import pycountry

# pandas Settings/Options
pd.set_option("display.max_rows", None) 
pd.set_option("display.max_columns", None)
pd.set_option('display.width', 9000)
pd.set_option('max_colwidth', 400)

# colormap 
heatmapCM = sns.color_palette('Spectral_r', as_cmap=True)


## directories 
DIR = os.getcwd()
print(f'{DIR=}')

DataDIR = os.path.join(DIR,'data')
OutDIR = os.path.join(DIR,'docs')

if not os.path.exists(DataDIR):
    print('***DATA FOLDER IS MISSING***')

if not os.path.exists(OutDIR):
    os.makedirs(OutDIR)

DIR='C:\\Users\\JGarza\\GitHub\\Excess_Mortality_And_Vaccines_In_Europe'

def df_column_uniquify(df):
    '''
    renames columns that are the same
    '''
    df_columns = df.columns
    new_columns = []
    for item in df_columns:
        counter = 0
        newitem = item
        while newitem in new_columns:
            counter += 1
            newitem = "{}_{}".format(item, counter)
        new_columns.append(newitem)
    df.columns = new_columns
    return df

def abbr_to_isoalpha3(abbr):
    """
    Convert a European country ISO Alpha-2 code to ISO Alpha-3 code.

    Parameters:
        abbreviation (str): ISO Alpha-2 country code (e.g., 'DE' for Germany).

    Returns:
        str: ISO Alpha-3 country code (e.g., 'DEU'), or None if not found.
    """
    try:
        country = pycountry.countries.get(alpha_2=abbr.upper())
        if country:
            return country.alpha_3
        else:
            return None
    except KeyError:
        return None

# this if for converting between the abbreviation andand the names of the countries

country_dict = {
    "BE": "Belgium",
    "BG": "Bulgaria",
    "CZ": "Czechia",
    "DK": "Denmark",
    "DE": "Germany",
    "EE": "Estonia",
    "IE": "Ireland",
    "EL": "Greece",
    "ES": "Spain",
    "FR": "France",
    "HR": "Croatia",
    "IT": "Italy",
    "CY": "Cyprus",
    "LV": "Latvia",
    "LT": "Lithuania",
    "LU": "Luxembourg",
    "HU": "Hungary",
    "MT": "Malta",
    "NL": "Netherlands",
    "AT": "Austria",
    "PL": "Poland",
    "PT": "Portugal",
    "RO": "Romania",
    "SI": "Slovenia",
    "SK": "Slovakia",
    "FI": "Finland",
    "SE": "Sweden",
    "IS": "Iceland",
    "LI": "Liechtenstein",
    "NO": "Norway",
    "CH": "Switzerland",
    "UK": "United Kingdom",
    "ME": "Montenegro",
    "GE": "Georgia",
    "AL": "Albania",
    "RS": "Serbia",
    "AD": "Andorra",
    "AM": "Armenia",
}

def abbr_to_name(abbreviation):
    return country_dict.get(abbreviation.upper(), "???")

def name_to_abbr(name):
    try:
        reverse_dict = {v: k for k, v in country_dict.items()}
        return reverse_dict.get(name.title(), "???")
    except:
        return "???"

# testing
print( abbr_to_name("BE") )  # Output: 'Belgium'
print( name_to_abbr("Belgium") )  # Output: 'BE'

Belgium
BE

# define now... put real values in later 
ddr2023 = None
ddr2024 = None
vdr2023 = None
vdravg  = None

def return_ddr2023_rank(abbr):
    '''
    the higher the value the higher the death 
    '''

    if ddr2023 is None:
        print('***DDR2023 DATA IS NOT LOADED***')
        return None
    
    for i, row in ddr2023.iterrows():
        if abbr == row['abbr']:
            return 1.0 - ( i/len(ddr2023) )

def return_vdr2023_rank(abbr):
    '''
    the higher the value the more vaccines
    '''

    if vdr2023 is None:
        print('***vdr2023 DATA IS NOT LOADED***')
        return None
    
    for i, row in vdr2023.iterrows():
        if abbr == row['abbr']:
            return 1.0 - ( i/len(ddr2023) )
        

def return_vdravg_rank(abbr):
    '''
    the higher the value the more vaccines
    '''

    if vdravg is None:
        print('***vdravg DATA IS NOT LOADED***')
        return None
    
    for i, row in vdravg.iterrows():
        if abbr == row['abbr']:
            return 1.0 - ( i/len(ddr2023) )
        
def return_vdravg_data(abbr):
    '''
    average vaccines between 2020-2023
    '''

    if vdravg is None:
        print('***vdravg DATA IS NOT LOADED***')
        return None
    
    for i, row in vdravg.iterrows():
        if abbr == row['abbr']:
            return row['avg2020_2023']

print(return_ddr2023_rank('MT'))
print(return_ddr2023_rank('CY'))

print(return_vdr2023_rank('MT'))
print(return_vdr2023_rank('CY'))

print(return_vdravg_rank('MT'))
print(return_vdravg_rank('CY'))

print(return_vdravg_data('MT'))
print(return_vdravg_data('CY'))

***DDR2023 DATA IS NOT LOADED***
None
***DDR2023 DATA IS NOT LOADED***
None
***vdr2023 DATA IS NOT LOADED***
None
***vdr2023 DATA IS NOT LOADED***
None
***vdravg DATA IS NOT LOADED***
None
***vdravg DATA IS NOT LOADED***
None
***vdravg DATA IS NOT LOADED***
None
***vdravg DATA IS NOT LOADED***
None

ddr2023 = pd.read_csv(os.path.join(OutDIR,'ddr2023.csv'))
ddr2024 = pd.read_csv(os.path.join(OutDIR,'ddr2024.csv'))
vdr2023 = pd.read_csv(os.path.join(OutDIR,'vdr2023.csv'))
vdravg = pd.read_csv(os.path.join(OutDIR,'vdravg.csv'))

display(ddr2023.head(5))
display(ddr2024.head(5))
display(vdr2023.head(5))
display(vdravg.head(5))

print(return_ddr2023_rank('MT'))
print(return_ddr2023_rank('CY'))

print(return_vdr2023_rank('MT'))
print(return_vdr2023_rank('CY'))

print(return_vdravg_rank('MT'))
print(return_vdravg_rank('CY'))

print(return_vdravg_data('MT'))
print(return_vdravg_data('CY'))

1.0
0.9696969696969697
0.6363636363636364
0.48484848484848486
0.7878787878787878
0.5151515151515151
63.682176806968585
53.34957190620881

# getting the data
cod = pd.read_excel(os.path.join(DataDIR,"hlth_cd_aro__custom_14850625_page_spreadsheet.xlsx"),sheet_name="Sheet 1")

# remove the headers
cod = cod.iloc[9::]

# drop the bad columns
for c in cod.columns:
    if pd.isnull(cod.at[9,c]):
        cod = cod.drop(columns=[c])

# rename time columns
for c in cod.columns:
    name = cod.at[9,c]
    cod = cod.rename(columns={c: name})

# make the duplicate column names unique
cod = df_column_uniquify(cod)

# # rename the first two columns
cod = cod.rename(columns={'TIME': 'name'})
cod = cod.rename(columns={'TIME_1':'cod'})

# drop, replace, reset index,
cod = cod.drop([9,10])
cod = cod.replace(to_replace=':', value=None)
cod = cod.reset_index(drop=True)

display(cod.head(10))

C:\Users\JGarza\pythons\Python312\Lib\site-packages\openpyxl\styles\stylesheet.py:237: UserWarning: Workbook contains no default style, apply openpyxl's default
  warn("Workbook contains no default style, apply openpyxl's default")

# lets get the abbr

cod['abbr'] = cod['name'].apply(name_to_abbr)

cod.loc[cod.name.str.contains('28 countries') == True,'abbr'] = 'Eur28'
cod.loc[cod.name.str.contains('27 countries') == True,'abbr'] = 'Eur27'

# France is France
cod.loc[cod.name.str.contains('Metropolitan France') == True,'abbr'] = 'Fr'

# Turkey is Tr
cod.loc[cod.name.str.contains('Türkiye') == True,'abbr'] = 'Tr'

# lets exclude the Eur28 and Eur27
cod = cod[~cod['abbr'].isin(['Eur28', 'Eur27'])]

display(cod.tail(250).head(10))
# display(cod.head(10))

# reformat the year

cod =  pd.melt(cod, id_vars=['name', 'abbr','cod'], var_name='year', value_name='deaths')

# we don't need cod total
cod = cod[cod.cod!= 'Total']

cod.year = pd.to_numeric(cod.year)
cod.deaths = pd.to_numeric(cod.deaths)
display(cod.head(10))

# filter out bad values

## old way of doing it
# cod = cod.replace([np.inf, -np.inf], np.nan).dropna()

cod = cod.map(lambda x: np.nan if x in [np.inf, -np.inf] else x).dropna()

# here we normalize the cause of death data

cod['deaths_norm'] = None
cod.reset_index(drop=True)

# print(cod.columns)

## baseline years are 2015,2016,2017 before the pandemic 
blyears = cod[cod.year.isin([2015,2016,2017])]
grouped = blyears.groupby(['name','abbr','cod'])

temp = grouped['deaths'].agg(
    baseline='mean',
).reset_index()

for _, row in temp.iterrows():
    try:
        cod.loc[(cod.abbr == row['abbr']) & (cod.cod == row['cod']) & (cod.name == row['name']), 'deaths_norm'] = cod[(cod.abbr == row['abbr']) & (cod.cod == row['cod']) & (cod.name == row['name'])].deaths / row['baseline']
    except Exception as e:
        # can't caluate them all 
        print(f"Error: {e} | {row=}")

cod.deaths_norm = pd.to_numeric(cod.deaths_norm)

# integrate Part 1 data

cod['ddr2023_rank'] = cod.abbr.apply(return_ddr2023_rank)
cod['vdr2023_rank'] = cod.abbr.apply(return_vdr2023_rank)
cod['vdravg_rank'] = cod.abbr.apply(return_vdravg_rank)

display(cod.head(10))

abbrs = ddr2023.abbr.head(100).to_list()

cod_counter = Counter()

for abbr in abbrs:
    temp = cod[cod.abbr == abbr]
    temp = temp.sort_values(by='cod',ascending=True)
    temp = temp.sort_values(by='year',ascending=False)

    death_rank = return_ddr2023_rank(abbr)
    vaccine_rank = return_vdr2023_rank(abbr)
    average_vaccine = return_vdravg_data(abbr)

    # get the average death norms for 2015,2016,2017,2018,2019 (prepandemic)
    grouped = temp[temp.year.isin([2015,2016,2017,2018,2019])].groupby('cod')
    dn00 = grouped.deaths_norm.agg(
        Mean='mean'
    ).reset_index()

    # get the average death norms for pandemic and postpandemic
    grouped = temp[temp.year.isin([2020,2021,2023])].groupby('cod')
    dn01 = grouped.deaths_norm.agg(
        Mean='mean'
    ).reset_index()

    # merge them 
    dn02 = pd.merge(dn00, dn01, on='cod', how='outer')
    # display(dn02)

    # only keep the ones where the Mean of pandemic and postpandemic is greater than prepandemic
    dn02 = dn02[ (dn02.Mean_x + 0.25) < dn02.Mean_y]
    
    cod_counter.update(dn02.cod.to_list())

    temp = temp[temp.cod.isin(dn02.cod)]

    title = f'{abbr_to_name(abbr)} {abbr} causes of death from 2015 - 2023* '
    display(MD(f'### {title}'))
    display(MD('#### these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023'))
    try:
        display(MD(f'#### {death_rank=:.4f},{vaccine_rank=:.4f},{average_vaccine=:.4f}'))
    except:
        pass

    fig = px.line(
        temp,
        x= 'year',
        y= 'deaths_norm',
        color='cod',
        hover_data=['abbr','name','cod','deaths_norm'],
        title=title,
        )
            
    
    fig.update_layout(template="plotly_dark")
    fig.show()

top =  20 
html_table = f"""
<h>top {top} Causes of Death (by appearence)</h>
<table>
    <tr>
        <th>Item</th>
        <th>Count</th>
        <th>bar</th>
    </tr>
    {''.join(f'<tr><td>{i[0]}</td><td>{i[1]}</td><td>{"#" * i[1]}</td></tr>' for i in cod_counter.most_common()[0:top])}
</table>
"""

common_cod = []
for i in range(top):
    common_cod.append(cod_counter.most_common()[i][0])

display(HTML(html_table))

temp = cod[cod.cod.isin(common_cod)]
temp =  temp.fillna(0)
# display(temp.head(5))

temp = pd.pivot_table(
    data = temp,
    values = 'deaths_norm',
    index = ['year','cod'],
    # columns=['year'],
    aggfunc='mean',
)
# temp = temp.sort_values(by='cod',ascending=True)
temp = temp.reset_index()
temp = temp.sort_values(by='year',ascending=False)

# display(temp)

fig = px.line(
    temp,
    x= 'year',
    y= 'deaths_norm',
    color='cod',
    hover_data=['year','cod','deaths_norm'],
    title=f"most common causes of death 2015-2023*",
    height=750
    )
        

fig.update_layout(template="plotly_dark")
fig.show()

print(*common_cod,sep='\n')

Ill-defined and unknown causes of mortality
Chronic liver disease (excluding alcoholic and toxic liver disease)
Drug dependence, toxicomania (F11-F16, F18-F19)
Other endocrine, nutritional and metabolic diseases (remainder of E00-E90)
Other diseases of the genitourinary system (remainder of N00-N99)
Other mental and behavioural disorders (remainder of F00-F99)
Dementia
Event of undetermined intent
Diabetes mellitus
Other external causes of morbidity and mortality (remainder of V01-Y89)
Falls
Other infectious and parasitic diseases (remainder of A00-B99)
Other symptoms, signs and abnormal clinical and laboratory findings (remainder of R00-R99)
Accidental poisoning by and exposure to noxious substances
Chronic viral hepatitis B and C
Certain infectious and parasitic diseases (A00-B99)
Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism
Other diseases of the circulatory system (remainder of I00-I99)
Non-malignant neoplasms (benign and uncertain)
Other accidents (W20-W64, W75-X39, X50-X59, Y86)

nw_cods = [
    
    # if you inject something into the blood, the blood and blood-forming organs could be effected
    'Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism',

    # the mRNA vaccine could effect DNA cand cuse this issue [wiki Turbo Cancer](https://en.wikipedia.org/wiki/Turbo_cancer)
    'Non-malignant neoplasms (benign and uncertain)',

    # blood is part of the circulatory system, so it makes sense that it could be effected
    'Other diseases of the circulatory system (remainder of I00-I99)',

    # the mRNA vaccine was still experimental, so new findings are expected
    'Other symptoms, signs and abnormal clinical and laboratory findings (remainder of R00-R99)',

    # if you weaken the immune system, it makes sense we would see an increase in inffections and parasites
    'Certain infectious and parasitic diseases (A00-B99)',
]

temp = cod[cod.cod.isin(nw_cods)]
temp =  temp.fillna(0)
# display(temp.head(5))

temp = pd.pivot_table(
    data = temp,
    values = 'deaths_norm',
    index = ['year','cod'],
    # columns=['year'],
    aggfunc='mean',
)
# temp = temp.sort_values(by='cod',ascending=True)
temp = temp.reset_index()
temp = temp.sort_values(by='year',ascending=False)

# display(temp)

fig = px.line(
    temp,
    x= 'year',
    y= 'deaths_norm',
    color='cod',
    hover_data=['year','cod','deaths_norm'],
    title=f"note worthy causes of death 2015-2023*",
    height=750
    )
        

fig.update_layout(template="plotly_dark")
fig.show()

	abbr	name	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
0	MT	Malta	1.004157	0.961827	1.033935	1.067663	1.071152	1.184449	1.203738	1.226999	1.169429	1.195514
1	CY	Cyprus	1.016240	0.943163	1.040284	1.003756	1.076467	1.129533	1.249761	1.247856	1.157833	1.095931
2	FI	Finland	0.983845	1.009937	1.006529	1.023471	1.011123	1.038642	1.083859	1.163166	1.150723	1.071371
3	IS	Iceland	0.975717	1.026691	0.998060	1.006559	1.012375	1.029265	1.045927	1.203398	1.147478	1.163664
4	NL	Netherlands	0.990752	0.999534	1.009892	1.031217	1.021614	1.133312	1.147735	1.140822	1.138502	1.149227

	abbr	name	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
0	MT	Malta	1.004157	0.961827	1.033935	1.067663	1.071152	1.184449	1.203738	1.226999	1.169429	1.195514
1	IS	Iceland	0.975717	1.026691	0.998060	1.006559	1.012375	1.029265	1.045927	1.203398	1.147478	1.163664
2	NL	Netherlands	0.990752	0.999534	1.009892	1.031217	1.021614	1.133312	1.147735	1.140822	1.138502	1.149227
3	LI	Liechtenstein	0.984876	1.050506	0.964909	1.062178	1.011598	1.240639	1.038833	1.085522	1.027161	1.127824
4	IE	Ireland	0.994335	1.004840	1.000934	1.020105	1.008454	1.028312	1.105698	1.135294	1.132809	1.113159

	abbr	name	2020	2021	2022	2023
0	FI	Finland	0.003119	123.689548	324.096693	272.896203
1	PT	Portugal	0.008074	94.765752	242.524514	206.590389
2	IT	Italy	0.004416	87.617659	233.812064	185.254382
3	SE	Sweden	0.002223	80.134686	207.824977	169.837101
4	FR	France	0.000154	85.965138	222.847236	123.812258

	name	cod	2015	2016	2017	2018	2019	2020	2021	2022	2023
0	European Union - 27 countries (from 2020)	Total	4618660	4536667	4660352	4693334	4660270	5189007	5298536	None	None
1	European Union - 27 countries (from 2020)	Certain infectious and parasitic diseases (A00-B99)	81735	74272	77707	77359	77128	73199	75560	None	None
2	European Union - 27 countries (from 2020)	Tuberculosis	4098	3809	3648	3597	3382	3041	2943	None	None
3	European Union - 27 countries (from 2020)	Viral hepatitis and sequelae of viral hepatitis	6956	6356	5857	4861	4396	4150	3587	None	None
4	European Union - 27 countries (from 2020)	Chronic viral hepatitis B and C	1127	4814	1020	4273	3830	3529	3028	None	None
5	European Union - 27 countries (from 2020)	Human immunodeficiency virus [HIV] disease	3234	2737	2438	2366	2173	2027	1891	None	None
6	European Union - 27 countries (from 2020)	Other infectious and parasitic diseases (remainder of A00-B99)	67284	61174	65666	66119	66788	64033	67197	None	None
7	European Union - 27 countries (from 2020)	Neoplasms	1200230	1213401	1212135	1216939	1218306	1215992	1193515	None	None
8	European Union - 27 countries (from 2020)	Malignant neoplasm of lip, oral cavity, pharynx	27968	28456	28285	28573	28094	27845	27571	None	None
9	European Union - 27 countries (from 2020)	Malignant neoplasm of colon, rectosigmoid junction, rectum, anus and anal canal	137921	139889	137718	137468	137428	135701	132364	None	None

	name	abbr	cod	year	deaths	deaths_norm	ddr2023_rank	vdr2023_rank	vdravg_rank
1	Belgium	BE	Certain infectious and parasitic diseases (A00-B99)	2015	2361.0	1.034921	0.333333	0.666667	0.69697
2	Belgium	BE	Tuberculosis	2015	57.0	1.315385	0.333333	0.666667	0.69697
3	Belgium	BE	Viral hepatitis and sequelae of viral hepatitis	2015	72.0	1.102041	0.333333	0.666667	0.69697
5	Belgium	BE	Human immunodeficiency virus [HIV] disease	2015	32.0	0.914286	0.333333	0.666667	0.69697
6	Belgium	BE	Other infectious and parasitic diseases (remainder of A00-B99)	2015	2200.0	1.033835	0.333333	0.666667	0.69697
7	Belgium	BE	Neoplasms	2015	28845.0	1.006525	0.333333	0.666667	0.69697
8	Belgium	BE	Malignant neoplasm of lip, oral cavity, pharynx	2015	588.0	1.002843	0.333333	0.666667	0.69697
9	Belgium	BE	Malignant neoplasm of colon, rectosigmoid junction, rectum, anus and anal canal	2015	2874.0	1.009129	0.333333	0.666667	0.69697
10	Belgium	BE	Malignant neoplasm of trachea, bronchus and lung	2015	6318.0	1.025372	0.333333	0.666667	0.69697
11	Belgium	BE	Malignant neoplasm of other parts of uterus	2015	364.0	0.934932	0.333333	0.666667	0.69697

	abbr	name	avg2020_2023
0	FI	Finland	180.171391
1	PT	Portugal	135.972182
2	IT	Italy	126.672130
3	SE	Sweden	114.449747
4	FR	France	108.156197

	name	cod	2015	2016	2017	2018	2019	2020	2021	2022	2023	abbr
1975	Norway	Intentional self-harm	598	627	603	681	666	650	657	None	None	NO
1976	Norway	Accidental poisoning by and exposure to noxious substances	324	348	298	331	323	376	322	None	None	NO
1977	Norway	Assault	25	36	30	24	31	36	31	None	None	NO
1978	Norway	Event of undetermined intent	7	5	5	6	8	12	12	None	None	NO
1979	Norway	Other external causes of morbidity and mortality (remainder of V01-Y89)	2	1	0	2	5	7	6	None	None	NO
1980	Switzerland	Total	68279	65533	67431	67621	68424	76853	71848	75199	None	CH
1981	Switzerland	Certain infectious and parasitic diseases (A00-B99)	849	765	825	823	826	851	912	1001	None	CH
1982	Switzerland	Tuberculosis	17	25	19	25	9	13	15	12	None	CH
1983	Switzerland	Viral hepatitis and sequelae of viral hepatitis	81	61	50	13	17	52	42	33	None	CH
1984	Switzerland	Chronic viral hepatitis B and C	None	None	0	9	11	6	7	11	None	CH

Item	Count	bar
Ill-defined and unknown causes of mortality	22	######################
Chronic liver disease (excluding alcoholic and toxic liver disease)	18	##################
Drug dependence, toxicomania (F11-F16, F18-F19)	17	#################
Other endocrine, nutritional and metabolic diseases (remainder of E00-E90)	17	#################
Other diseases of the genitourinary system (remainder of N00-N99)	15	###############
Other mental and behavioural disorders (remainder of F00-F99)	15	###############
Dementia	13	#############
Event of undetermined intent	13	#############
Diabetes mellitus	12	############
Other external causes of morbidity and mortality (remainder of V01-Y89)	12	############
Falls	11	###########
Other infectious and parasitic diseases (remainder of A00-B99)	9	#########
Other symptoms, signs and abnormal clinical and laboratory findings (remainder of R00-R99)	8	########
Accidental poisoning by and exposure to noxious substances	7	#######
Chronic viral hepatitis B and C	7	#######
Certain infectious and parasitic diseases (A00-B99)	6	######
Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism	6	######
Other diseases of the circulatory system (remainder of I00-I99)	6	######
Non-malignant neoplasms (benign and uncertain)	6	######
Other accidents (W20-W64, W75-X39, X50-X59, Y86)	5	#####

Excess Mortality in Europe and Why (part 2)¶

Background information¶

the Covid-19 Vaccine¶

(Hypothesis) What are the medical issues we can we expect to see?¶

SetUp¶

Helping Functions¶

import data from part 1¶

Variables¶

Cause of Death Data¶

Source¶

Downloading Instructions¶

Description¶

variables¶

viewing some data¶

Malta MT causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=1.0000,vaccine_rank=0.6364,average_vaccine=63.6822¶

Cyprus CY causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.9697,vaccine_rank=0.4848,average_vaccine=53.3496¶

Finland FI causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.9394,vaccine_rank=1.0000,average_vaccine=180.1714¶

Iceland IS causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.9091,vaccine_rank=0.1818,average_vaccine=49.0175¶

Netherlands NL causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.8788,vaccine_rank=0.7273,average_vaccine=56.5856¶

Ireland IE causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.8485,vaccine_rank=0.7576,average_vaccine=60.8369¶

Germany DE causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.8182,vaccine_rank=0.5758,average_vaccine=57.4140¶

Denmark DK causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.7879,vaccine_rank=0.7879,average_vaccine=62.6449¶

Austria AT causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.7576,vaccine_rank=0.3333,average_vaccine=50.1820¶

Luxembourg LU causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.7273,vaccine_rank=0.5152,average_vaccine=52.3950¶

Portugal PT causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.6970,vaccine_rank=0.9697,average_vaccine=135.9722¶

Slovenia SI causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.6667,vaccine_rank=0.3030,average_vaccine=38.3650¶

Switzerland CH causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

Norway NO causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.6061,vaccine_rank=0.5455,average_vaccine=56.6662¶

France FR causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.5758,vaccine_rank=0.8788,average_vaccine=108.1562¶

Greece EL causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.5455,vaccine_rank=0.6061,average_vaccine=53.5063¶

Spain ES causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.5152,vaccine_rank=0.6970,average_vaccine=58.4003¶

Estonia EE causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.4848,vaccine_rank=0.3939,average_vaccine=43.9019¶

Poland PL causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.4545,vaccine_rank=0.8182,average_vaccine=79.3814¶

Liechtenstein LI causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.4242,vaccine_rank=0.4545,average_vaccine=50.0819¶

Czechia CZ causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.3939,vaccine_rank=0.4242,average_vaccine=46.3955¶

Italy IT causes of death from 2015 - 2023*¶

these are only causes of death where Average of death norms from 2015 - 2019 + 0.25 is less than Average of death norms from 2020 - 2023¶

death_rank=0.3636,vaccine_rank=0.9394,average_vaccine=126.6721¶

Belgium BE causes of death from 2015 - 2023*¶